Skip to main content
Back to E-commerce Dictionary

Data validation

Data management and qualityBasic Level

Data validation is the automatic check that product data is accurate, complete and correctly formatted before it is saved, imported or sent to a sales channel.

Image by · CC BY 4.0

What is Data validation?

Data validation is the check that product data is correct, complete and in the right format before it is saved, imported or published. A product data validation tool runs those checks automatically against rules you define, such as "price must be a number" or "every product needs a GTIN". Validation gives a pass or fail per rule. That makes it different from data profiling, which first measures what your data looks like, and from data cleansing, which fixes the errors that validation finds. In an online store, most checks cover prices, identifiers, required attributes, image links and the field rules of each sales channel. To see which errors Google would flag in your product feed, run it through the free Google Shopping feed checker.

Why Data validation matters for e-commerce

An online store publishes whatever is in its product data. A price stored as text, a missing size or a broken image link ends up in front of shoppers and in marketplace feeds, where it causes rejected listings, wrong orders and returns. Validation stops those errors at the door. It is cheaper to block one bad value during an import than to correct hundreds of live listings across several channels afterward.

Free tool

Check a product against Google Shopping rules

Enter one product's title, description, price, GTIN and image link. The checker lists the errors and warnings Merchant Center's core requirements would raise, before you upload.

Open the full tool
Loading the tool

Examples of Data validation

  • 1Field: Price, Rule: positive number with two decimals, Fails on: "12,99 EUR" stored as text
  • 2Field: GTIN, Rule: 8, 12, 13 or 14 digits with a valid check digit, Fails on: a 12-digit code with a wrong last digit
  • 3Field: Brand, Rule: value from the approved brand list, Fails on: "Nike Inc" when the list says "Nike"
  • 4Field: Image URL, Rule: link returns an image file, Fails on: a link that returns a 404 page
Image by · CC BY 4.0

How WISEPIM Helps

  • Quality Guard rules: set checks for required fields, formats and value lists, and see which products fail them.
  • Channel Readiness: see per sales channel which products still miss data that the channel requires.
  • Bulk editing: fix the same error on many products at once instead of one record at a time.
  • Rollback Import: undo an import that brought in bad data, so you can correct the file and try again.

Common mistakes with Data validation

  • Starting without written rules. If nobody agreed what a valid value is, the checks cannot be consistent.
  • Validating only at first entry. Updates, imports and integrations bring in errors later too.
  • Making rules so strict that the team cannot save basic records, which leads to workarounds.
  • Skipping channel rules. A product can be valid in your PIM and still be rejected by a marketplace.
  • Ignoring feedback from the people who enter data when a rule keeps blocking them.

Tips for Data validation

  • Write down your data rules before you configure them, so everyone works to the same standard.
  • Start with the fields that affect sales and shipping: price, SKU, GTIN, weight and stock.
  • Write error messages that say what is wrong and how to fix it, not just an error code.
  • Check exports against channel rules too, for example with the Google Shopping feed checker.
  • Review your rules every quarter and remove the ones nobody needs anymore.

Trends around Data validation

  • AI that suggests validation rules from patterns in existing data and flags outliers.
  • Validation tied to channel requirements, so each marketplace gets its own rule set.
  • Checks that run continuously on the live catalog, not only during imports.
  • New rule sets for sustainability and compliance data, such as materials and certifications.

Tools for Data validation

  • WISEPIM: a PIM with Quality Guard rules and Channel Readiness to check product data before it goes to a channel.
  • Akeneo PIM: a PIM with completeness rules and attribute constraints per family.
  • Salsify: a product experience platform with validation against retailer requirements.
  • Talend: a data integration tool with profiling, cleansing and validation steps.
  • Google Merchant Center: shows diagnostics for feed items that fail Google's product data rules.

Related Terms

Access Control (PIM)Attribute Dependency ManagementAttribute InheritanceAttribute ManagementAttribute mappingAttribute Scoping

Also Known As

Product data validationData integrity checksData quality controlInput validation

Frequently Asked Questions

It is software that checks product records against rules, such as required fields, number formats and allowed values, and lists every record that fails. PIM systems, feed tools and marketplace dashboards all include validation. A spreadsheet with formulas works for small catalogs but gets hard to maintain.

Define rules for each important field, run them on every import and before every export, and fix what fails. Check channel rules as well, because Google, marketplaces and comparison sites each have their own required fields. Validate again after bulk changes.

Validation checks whether a value follows the rules, for example a GTIN with the right number of digits. Verification checks whether the value is true, for example by comparing it with the supplier's spec sheet. You usually need both.

The common types are type checks (number, text, date), format checks, range checks, uniqueness checks for SKUs, lookup checks against a fixed list and presence checks for required fields. Most product catalogs use all six.

Still have questions?

Can't find the answer you're looking for? Please get in touch with our team.

Contact Support

Keep exploring

Hand-picked next steps to go deeper.